A system for neuropsychology-based dynamic content customization and a method thereof

Abstract

The invention pertains to a content customization system (100) that provides dynamically personalised content utilising a multifaceted understanding of user behaviour and preferences. The system (100) integrates artificial intelligence with psychological and neuroscientific insights to tailor content, such as text, images, audio etc., to individual users in real-time. The system (100) comprises a Multifaceted Understanding Engine (MUE) (102) that processes the user data through a Neuro Psycho Artificial Intelligence (NPAI) module (106) to generate a dynamic psychological profile of the user, based on which a specialized prompt generation module (107) generates content customization instructions or "prompts" to retrieve content from a repository server (111) that further gets refined by utilizing Large language model (112), thereby delivering a seamless and personalized content experience to the user in real time.

Description

TITLE OF THE INVENTIONA system for neuropsychology-based dynamic content customization and a method thereofPriority Claim:

[0001] This application claims priority from the provisional application numbers: a. 202441002757 filed with Indian Patent Office, Chennai on 13thJanuary 2024, entitled “ NeuroP sychoAI: Al-Powered Personalized Dynamic Content Customizer with Psychological and Neuroscientific Insights'", and b. 202441002781 filed with Indian Patent Office, Chennai on 14thJanuary 2024, entitled “ NeuroP sychoAI: Al-Powered Personalized Dynamic Content Customizer with Psychological and Neuroscientific Insights", the entirety of which are expressly incorporated herein by reference.Preamble to the Description

[0002] The following specification describes the invention and the manner in which it is to be performed:DESCRIPTION OF THE INVENTIONField of Invention:

[0003] The present invention relates to the field of adaptive content customization technology. It is concerned with the development of a dynamic and intelligent system that leverages Artificial Intelligence (Al), psychology and neuroscience insights to personalise and optimise content across diverse domains. The presentinvention further discloses a method for neuropsychology-based dynamic content customization based on the preferences and cognitive patterns of individual users.Background of the invention

[0004] Traditional content customization systems typically adopt a 'one-size-fits- alf approach, presenting uniform content regardless of individual users' diverse needs, preferences, and levels of understanding.

[0005] This static approach often falls short in enabling users to comprehend and engage effectively with the content. For instance, in an online learning scenario, a single teaching method or material may not suit all students, leading to a lack of personalised learning experiences.

[0006] Several attempts have been made to address these limitations. For instance, the patent application No. KR102191670B1 titled “Personalized learning method based on test question bank and knowledge map data and system ” discloses a system that uses knowledge maps and question banks to create a personalized learning experience for a user. The system works by building the knowledge maps of learning elements, incorporating curriculum and learning content from various sources, and generating questions based on this framework. The system refines the question bank to deliver customized learning experiences to users through an iterative process of testing and feedback.

[0007] The Patent Application No. IN202311054572A titled “Adaptive learning system for professional development and skill acquisition ” discloses an educational platform that transforms the method of skill development and knowledge acquisition. The system offers personalized learning experiences to individuals seeking to enhance their professional skills by combining artificial intelligence (Al) with adaptive learning techniques. The learners undergo an initial proficiency assessment, allowing the system to create personalized learning paths based on their strengths, weaknesses, and goals. The system dynamically adjusts the content and pace of learning materials in real-time, customizing the experience to match each learner's requirements. Continuous assessment and feedback mechanisms facilitateprogress tracking, while data analytics provide valuable insights for continuous improvement.

[0008] The Patent application No. IN202441050764A titled “Dynamic content personalization engine for web applications” discloses a system for a real-time personalization of web application content, utilizing machine learning algorithms to enhance a user engagement, and the corresponding method. The system dynamically generates personalized content recommendations by analysing the user behaviours such as clicks, searches, and time spent on pages. The adaptive engine integrates with web applications and continuously learns from user behaviour to improve personalization, enhancing user engagement and retention.

[0009] Despite the advancements in the field, currently, there is no system capable of intelligently and dynamically adapting content (not limited to text, images, audio etc) in real-time to align with the unique cognitive and behavioural patterns of each user. Hence, there remains a need for a system, capable of understanding the psychological and cognitive patterns of every individuals to enable a real-time content creation across various formats that further ensures optimal user engagement and comprehension. Such a system would offer a truly personalised and optimised experience, tailored to individual requirements.Object of the Invention:

[0010] The present invention seeks to provide a solution to this problem(s) by providing personalised, dynamic content, leveraging Artificial Intelligence, psychology and neuroscience. Its primary objective is to utilise Al algorithms, psychological assessments and neuroscientific insights for an in-depth, multifaceted understanding of users, surpassing mere cognitive analysis. The system includes specialised prompt generators for content customization across various formats like text, images, audio, and animations, tailored to individual cognitive and behavioural patterns. This flexibility extends to other formats as well, enhancing content customization capabilities.Statement of the Invention:

[0011] This invention introduces an advanced system for dynamically customising content in various formats, such as text, images, and audio. The system innovatively combines Artificial Intelligence (Al), psychology, and neuroscience to enable realtime, user-centric content adaptation. It comprises two main components:

[0012] Multifaceted Understanding Engine (MUE): This engine features a “Neuro Psycho Artificial Intelligence (NPAI) module”, a pioneering module in personalization technology. The Neuro Psycho Artificial Intelligence (NPAI) module processes diverse optional inputs, psychological assessments, user selfdeclarations, Al-driven real-time feedback utilising computer vision and natural language processing models, and data from non-invasive neuroimaging devices.

[0013] Specialized Prompt Generation Module (SPG): This component of the system works closely with the MUE. Based on the user's needs and preferences as identified by the MUE, along with content extracted from existing sources using semantic search technologies, the Specialized Prompt Generation Module (SPG) creates specific instructions, or 'prompts'.

[0014] The prompts obtained from the SPG help large language models (LLMs), to tailor and modify the content so that it aligns perfectly with what each user might find most relevant and engaging.

[0015] This invention establishes a new paradigm in content personalization, leveraging a unique blend of Al, psychology, and neuroscience to offer a comprehensive, real-time, and user-focused experience in content customization. The unique blend of Al, psychology, and neuroscience, as applied in the system, marks a significant departure from existing methodologies by providing a dynamic, real-time adaptation of content based on a multifaceted understanding of individual user parameters. This approach not only bridges the gap found in current technologies, which often rely on static or semi-dynamic adaptation methods, but also introduces advanced functionalities like integration with non-invasive neuroimaging devices and real-time feedback using sophisticated Al algorithms. The result is highly responsive and personalised, that significantly enhances user engagement and comprehension.Summary of the Invention:

[0016] The present invention introduces a cutting-edge system for real-time, userspecific content customization, including but not limited to, formats like text, images, and audio. The system uniquely integrates Artificial Intelligence (Al), psychology, and neuroscience through two main components: the Multifaceted Understanding Engine (MUE) with the Neuro Psycho Artificial Intelligence (NPAI) module for processing diverse user data, and the specialized prompt generation module (SPG) for creating prompts that guide large language models in personalising content. The MUE processes multiple input data received through a real-time feedback module, a psychological assessments module, and a user selfdeclarations module through the incorporation of a Neuro Psycho Artificial Intelligence (NPAI) module that employs machine learning algorithms to analyse multiple input data to create a comprehensive and dynamic psychological profile of the user.

[0017] Further, the specialized prompt generation module (SPG) comprises a NPAI profiling unit, a content extraction unit, and a prompt devising unit to create specialized content customization instructions or prompts to retrieve contextually relevant content from a repository server using the methodologies of the semantic search. Further, the large language model generates a dynamically adapted content, based on the user-specific dynamic psychological profile and preferences. Moreover, a user output interface module presents the customized content to the user in various formats based on the user preferences.

[0018] The system implements an active learning mechanism that continuously refines content customization by analysing the user responses and feedbacks. The system's adaptive approach ensures a precise personalization by dynamically prioritizing input parameters based on the needs of the individual user.

[0019] The present invention further discloses a method for neuropsychologybased dynamic content customization. The method comprises receiving input data from various sources such as psychological assessments, user self-declarations, and one or inputs from a neuroimaging device, visual feedback from image capturingdevice, and textual feedback captured through the user interactions. The input data is processed through a Multifaceted Understanding Engine (MUE) that integrates the psychological, behavioral, and neurological inputs of the user. The input data is further analysed through a Neuro Psycho Artificial Intelligence (NPAI) module that employs an active learning mechanism to dynamically generate a psychological profile of the user. Further, a contextually relevant content is retrieved from a repository server using a semantic search methodology. Further, specialized prompts are generated using a NPAI profiling unit, a content extraction unit, and a prompt devising unit that are processed through at least one Large Language Model (LLM) to dynamically adapt the personalized content in multiple formats, such as a text, an audio, or a visual output, ensuring versatility in content delivery through a user output interface module, and collecting multi-modal feedback for continuous adaptation.

[0020] This innovative approach offers a novel and comprehensive method for dynamic content personalization, tailored to individual user needs and preferences.Brief description of the drawings

[0021] The foregoing and other features of embodiments will become more apparent from the following detailed description of embodiments when read in conjunction with the accompanying drawings. In the drawings, like reference numerals refer to like elements.

[0022] Figure 1 illustrates a block diagram of a system for neuropsychology-based dynamic content customization, in accordance with an embodiment of the invention.

[0023] Figure 2 illustrates a flowchart of a method used in a neuropsychologybased dynamic content customization, in accordance with an embodiment of the invention.Detailed description of the invention

[0024] The present invention discloses a neuropsychology-based dynamic content customization system that personalizes content in real-time by integrating the psychological, behavioural, and neurological inputs from a user. The system comprises a Multifaceted Understanding Engine (MUE) that processes multiple user data to create a dynamic user profile. The system further comprises a specialized prompt generation module with a NPAI profiling unit, a content extraction unit, and a prompt devising unit working in conjunction with a repository server and at least one large language model to generate a personalized content based on the dynamic user profile, and deliver them through a user output interface module.

[0025] The present invention further discloses a method for dynamically customizing content by receiving a multiple user inputs, processing the user inputs to generate a dynamic psychological profile, retrieving relevant content from a repository server, generating customization instructions or prompts, processing these prompts through large language models to create a personalized content and delivering the personalized content while collecting real-time feedback for iterative adaptation.

[0026] Figure 1 illustrates a block diagram of a system for neuropsychology-based dynamic content customization, in accordance with an embodiment of the invention. The system (100) comprises a user input interface module (101) to receive input data from a user. In one embodiment, the user input interface module (101) is implemented as a Graphical User interface (GUI)-based interface that facilitates seamless data collection in various input formats such as a text, an audio or a video input, ensuring comprehensive data capture from the user.

[0027] Further, the system (100) comprises a Multifaceted Understanding Engine (MUE) (102) that processes and analyses various input data received from a realtime feedback module (103) that captures dynamic user data, a psychological assessment module (104) that captures and assesses the psychological state of the user and a user self-declaration module (105) that captures the user-provided information.

[0028] The real-time feedback module captures dynamic user data from at least one of the connected devices such as one or more neuroimaging devices (103A) that record the neural activity of the user, one or more visual data capture devices (103B) that register the facial expressions and behavioural cues, and one or more textual data capture devices (103C) that capture the textual interactions of the user.

[0029] In an embodiment, the neuroimaging device (103A) utilizes different non- invasive neuroimaging instruments such as an ElectroEncephaloGraphy (EEG) sensor, a functional Near-InfraRed Spectroscopy (fNIRS) device, or an wearable brain-computer interface device to capture a continuous feed of the user’s physiological data, comprising the neural patterns, heart rate variability, and other biometrics related to stress and cognitive focus of the user.

[0030] In another embodiment, the visual data capture device (103B) comprises a camera to capture the facial expressions, eye movements, and different behavioural cues of the user in real-time. In yet another embodiment, the textual data capture device (103C) captures the user’s textual engagement through the interactions with a chatbot, or different social media posts. The inputs from the textual data capture device (103C) undergo semantic and sentiment analysis to comprehend the user intent and emotional tone.

[0031] Further, the psychological assessment module (104) processes different standardized personality assessments, cognitive evaluations, and behavioural patterns of the user that provide a foundational understanding of the personality traits, cognitive processing styles, and potential behavioural patterns of the user.

[0032] Furthermore, the user self-declaration module (105) collects and process user- specified preferences, learning goals, and interaction expectations during initial registration and periodic updates. The user self-declaration module (105) serves as a static input that informs the system (100) of any pre-existing biases or expectations of the user that influences content interpretation and reception.

[0033] The MUE (102) further comprises a Neuro Psycho Artificial Intelligence (NPAI) module (106) that receives data from the real-time feedback module (103),the psychological assessment module (104) and the user self-declaration module (105) to create a dynamic psychological profile of the user. The NPAI module (106) further employs an active learning mechanism to identify and fine-tune the weightage of the each input parameters received from the real-time feedback module (103), the psychological assessment module (104) and the user selfdeclaration module (105) based on the preferences and emotional state of the user, thereby enhancing the personalization accuracy of the content generation.

[0034] The system (100) further comprises a prompt generation module (107) that generates customised prompts for personalizing the content generation. The prompt generation module (107) comprises an NPAI profiling unit (108) that processes the dynamic psychological profile of the user, a content extractor unit (109) to retrieve relevant content from a repository server (111), and a prompt devising unit (110) to formulate multiple instructions for the content customization.

[0035] The NPAI profiling unit (108) receives the dynamic psychological profile of the user from the NPAI module (106) to interpret multiple physiological and psychological factors such as the emotional intensity, focus, and stress levels of the user, and transmit them to the content extractor unit (109). The content extractor unit (109) employs semantic search methodologies to identify and dynamically retrieve the contextually relevant content from the repository server (111) and further transmit them to the prompt devising unit (110). The repository server (111) comprises distributed databases to store various content formats including text, images, audio, and video content.

[0036] Further, the prompt devising unit (110) connected with at least one Large Language Model (LLM) (112) formulates the final prompt for instructing the LLM to adapt and generate the personalized content, ensuring alignment with the unique psychological profile of the user delivered by the NPAI profiling unit (108).

[0037] The prompt generation module (107) comprising the NPAI profiling unit (108), the content extractor unit (109), and the prompt devising unit (110) operate with an active learning mechanism, for refining prompts over time based on the user responses and recording the preferences through ongoing interactions. Theprompt generation module (107) interacts closely with the NPAI module (106) that provides a continuously updated, dynamic psychological profile of the user. The real-time psychological profile allows the prompt generation module (107) to adjust the content responsively based on the user’s cognitive and emotional state, ensuring high-level personalization that evolves over time.

[0038] The system (100) further comprises a user output interface module (113) to present the customized content to the user in various formats based on user preferences and device capabilities, facilitating their continuous engagement.

[0039] Figure 2 illustrates a flowchart of a method used in a neuropsychologybased dynamic content customization, in accordance with an embodiment of the invention. The method (200) for neuropsychology-based dynamic content customization comprises the steps of receiving multiple input data comprising a psychological assessment, a user self- declaration and one or more real-time feedback data from one or more neuroimaging, visual, and textual data capture devices in step (201). Step (201) ensures the collection of comprehensive data necessary for subsequent analysis and personalization. In step (202), the input data is processed using a Multifaceted Understanding Engine (MUE) that integrates the psychological, behavioural, and neurological inputs of the user. In an embodiment, the processing of the input data comprises pre-filtering and normalization of the input data to improve the accuracy of subsequent analyses. In step (203), the input data is analysed through a Neuro Psycho Artificial Intelligence (NPAI) module that employs an active learning mechanism to dynamically generate a psychological profile of the user that continuously gets updated during the user interaction with the system, further allowing the NPAI module to adapt to the continuously evolving cognitive and emotional state of the user. In step (204), a contextually relevant content is retrieved from a repository server using one or more semantic search methodologies. The retrieved content matches the dynamic psychological profile of the user ensuring the content’s alignment with the user’s cognitive and emotional preferences. In an embodiment, the semantic search employs a natural language processing algorithm to extract an engaging and relevant content. In step (205), specialized prompts are generated through the utilization of a NPAI profiling unit,a content extraction unit, and a prompt devising unit. These units collaborate to create customised prompts that guide content adaptation based on the dynamic psychological profile of the user. The prompts are further optimized for tone, complexity, and pacing to suit the current engagement level of the user. In step (206), the generated prompts are processed through at least one Large Language Model (LLM) to dynamically adapt the personalised content. The LLM customizes the content format and delivery style to align with the user’s specific preferences and cognitive requirements in multiple formats, such as text, audio, or visual outputs, ensuring versatility in content delivery. In step (207), the personalized content is presented to the user through an output interface module. Further, multimodal feedbacks are dynamically collected to refine the performance, and improve future personalization.

[0040] The present invention offers significant advancements in the field of content customization by integrating the neuropsychological, behavioural, and neurological inputs of a user to deliver a personalized and real-time content. Firstly, the system (100) dynamically receives inputs from multiple sources such as psychological assessments, user self-declarations, neuroimaging feedback, visual data, and / or textual inputs to generate a dynamic psychological profile of the user utilizing a Multifaceted Understanding Engine (MUE) (102) and employing the semantic search methodology for retrieving contextually relevant content ensuring its precise alignment with the user's cognitive and emotional preferences. Further, the prompt generator module (107) and the Large Language Model (LLM) (112) enable seamless personalized content generation across multiple formats, including text, audio, and video. The system (100) further employs iterative feedback integration, allowing the generated content to evolve according to the dynamically changing profile and needs of the user, thereby enhancing engagement and comprehension.Potential Applications of the System (100)

[0041] The system (100), with its innovative integration of Al, psychology, and neuroscience, has a wide range of potential applications across various sectors, demonstrating its versatility and significance in addressing diverse needs. Some ofthe key areas where this system (100) is particularly beneficial include, but not limited to: a. Education: In the educational domain, the system (100) can bring truly personalised learning. By adapting educational content to align with individual students' learning styles and cognitive patterns, the system (100) can enhance comprehension, retention, and engagement. It can be particularly beneficial for students with specific learning needs, providing them with tailor-made educational materials that cater to their unique learning processes. b. Marketing and Advertising: In marketing, this system (100) can be used to create highly personalised advertising content. By understanding consumer behaviour and preferences at a granular level, the system (100) can dynamically generate marketing materials that resonate more deeply with individuals, leading to increased engagement and conversion rates. c. Mental Health: The system (100) has significant applications in the mental health sector. By leveraging psychological assessments and neuroscientific data, it can assist in creating personalised therapeutic content, such as guided meditations or cognitive behavioural therapy exercises, tailored to the mental health needs of individuals. d. Content Creation and Media: For content creators and media companies, the system (100) offers a tool to dynamically adapt content to suit the preferences and engagement patterns of different audiences, thereby increasing viewership and user satisfaction. e. Corporate Training and Development: In the corporate world, this system (100) can be used for personalised employee training and development programs. By customising training materials to suit different learning styles and professional requirements, the system (100) ensures more effective and efficient skill development.f. E-Commerce: In e-commerce, the system (100) can enhance user experience by personalising product recommendations and content, based on deep insights into consumer behaviour and preferences, thus driving sales and customer loyalty. g. Adaptive UI / UX Customization: The system (100) employs real-time adaptation of user interface elements in digital applications to align with the individual user preferences and cognitive patterns, enhancing usability and user satisfaction.

[0042] By addressing these diverse application areas, the system (100) not only demonstrates its wide-ranging utility but also its potential to positively impact various aspects of society and industry.Legal Compliance and Ethical Considerations

[0043] The system (100) offers high priority on ensuring user consent and data privacy and security.

[0044] User Consent: Central to our approach is obtaining explicit and informed consent from users. Prior to data collection, users are clearly informed about the nature of the data being collected and its intended use.

[0045] Data Privacy and Security: To safeguard the user data, all collected information are stored securely as per the industry standard and concerning law of the land.

[0046] By adhering to these principles, the system (100) ensures a foundation of ethical use and robust protection of user data.

[0047] Having generally described this invention, a further understanding can be obtained by reference to a specific example, which is provided herein for the purpose of illustration only and is not intended to be limiting unless otherwise specified.Example 1: A demonstrative illustration of enhancing mental well-being of a user through a personalized guided meditation:

[0048] As an illustrative example, consider a volunteer, a working professional is experiencing high stress due to her demanding schedule. She uses the system (100) to participate in a guided meditation session customized to her cognitive and emotional needs. The volunteer begins interacting with the system (100) through the user input interface module (101) for login and completes a short survey through the user self-declaration module (105), where she provides her preference for a calming audio -visuals, featuring a theme related to the nature. She further declares her primary goal to be the reduction of stress. Concurrently, the psychological assessment module (104) administers standardized tests, such as the Perceived Stress Scale (PSS), a widely used psychological instrument for measuring the perception of stress and Big Five Personality Traits Assessment, a psychological test that measures five key dimensions of personality: openness, conscientiousness, extraversion, agreeableness, and neuroticism to provide an insight of the behavioural and emotional tendencies of any individual.

[0049] The results from the psychological assessment module (104) and the user self-declaration module (105) are transmitted to the Neuro Psycho Artificial Intelligence (NPAI) module (106) for further assessments.

[0050] During the session, the volunteer wears a non-invasive EEG headband, functioning as the neuroimaging device (103A) that captures real-time brain activity, stress level and her relaxation state. Additionally, a camera, acting as the visual data capture device (103B) monitors her facial expressions, capturing subtle changes such as furrowed brows or relaxed features to assess her engagement and emotional responses, and a textual data capture device (103C) collects the typed feedback she provides during the session.

[0051] The data from the multiple inputs (103A, 103B, 103C, 104, 105) are transmitted and processed by the NPAI module (106) to generate a dynamic psychological profile of the volunteer based on which the content extraction unit (109) retrieves content from a repository server (111) related to the meditation thatsuited her needs. The retrieved content is transmitted to the prompt devising unit (HO) that further creates and transmits specialized instructions or “prompts” to a Large Language Model (LLM) (112) to dynamically refine the content extraction and supply for the her meditation session. For instance , on detection of an elevated stress levels through the visual data capture device (103B) and the neuroimaging device (103A), the prompt generation module (107) adjusts the prompt to generate content that reduces the cognitive load, modify the content tone, and simplify the language for easier comprehension. Conversely, on detection of a high focus and low stress, the prompt generation module (107) generate prompts that deliver a more detailed, complex content, leveraging the user's optimal engagement state for the in-depth content. The session is displayed to the volunteer through the user output interface module (113), presenting the synchronized visuals on her device screen while playing the audio through her headphones.

[0052] Along the progression of the session, the real-time feedback is continuously collected through the neuroimaging device (103A) that monitors her decreasing stress levels, the visual data capture device (103B) records her relaxed facial expressions, and the textual data capture device (103C) logs her typed feedback, such as her appreciation for the breathing exercises. This feedback is fed back into the system (100), allowing for iterative adjustments..Reference numbers:

Claims

ClaimsWe claim:

1. A neuropsychology-based dynamic content customization system, the system (100) comprising: a. a user input interface module (101) to receive one or more input data from a user; b. a Multifaceted Understanding Engine (MUE) (102) connected to the user input interface module (101) to process the input data, wherein the MUE (102) comprises: i. a real-time feedback module (103) to capture one or more feedbacks from the user in real-time and transmit to a neuro psycho artificial intelligence (NPAI) module (106); ii. a psychological assessment module (104) to capture, process, and transmit a psychological assessment data of the user to the NPAI module (106); iii. a user self-declaration module (105) to capture the user- provided preferences and transmit to the NPAI module (106); wherein the NPAI module (106) processes the input data received from the real-time feedback module (103), the psychological assessment module (104), and the user self-declaration module (105) to generate a dynamic psychological profile of the user; c. a specialized prompt generation module (107) connected to the MUE (102) to generate one or more instructions for content customization based on a dynamic psychological profile of the user; d. at least one Large Language Model (LLM) (112) connected to the specialized prompt generation module (107) to generate a personalized content based on the one or more content customization instructions received from the specialized prompt generation module (107); ande. a user output interface module (113) connected to the large language model (112) to display the personalized content to the user.

2. The system (100) as claimed in claim 1, wherein the real-time feedback module (103) comprises at least one of the following: i. at least one neuroimaging device (103A) connected to the user to capture one or more physiological data of the user in real-time; ii. at least one visual data capture device (103B) to capture a facial expression of the user; and iii. at least one textual data capture device (103C) to capture a textual interaction of the user.

3. The system (100) as claimed in claim 1, wherein the plurality of input data processed through the MUE (102) comprises a psychological assessment data received from a psychological assessment module (104), a user selfdeclaration data received from a user self-declaration module (105), and one or more real-time feedback data from the real-time feedback module (103).

4. The system (100) as claimed in claim 1, wherein the MUE (102) processes the psychological, behavioural, and real-time feedback data of the user to integrate for further generating a dynamic psychological profile of the user.

5. The system (100) as claimed in claim 1, wherein the specialized prompt generation module (107) comprises: i. an NPAI profiling unit (108) connected to the NPAI module (106) to receive and process the dynamic psychological profile of the user, and transmit to a content extractor unit (109); ii. the content extractor unit (109) connected to at least one repository server (111) to retrieve one or more content and transmit to a prompt devising unit (110); andiii. the prompt devising unit (110) connected to at least one LLM (112) to formulate and transmit one or more content customization instructions; wherein the LLM (112) generates a personalized content in multiple formats, including one or more text, audio, and visual media.

6. The system (100) as claimed in claim 5, wherein the content extractor unit (109) employs a natural language processing (NLP) based semantic search methodology to retrieve the content from the repository server (111) based on the dynamic psychological profile of the user.

7. A method for a neuropsychology-based dynamic content customization, the method (200) comprising the steps of: a. receiving multiple input data comprising a psychological assessment, a user self-declaration and one or more real-time feedback data from one or more neuroimaging, visual, and textual devices (201); b. processing the input data using a Multifaceted Understanding Engine (MUE) to integrate the psychological, behavioural, and real-time feedback data of the user (202); c. analysing the processed user data through a Neuro Psycho Artificial Intelligence (NPAI) module with an active learning mechanism to generate a dynamic psychological profile (203); d. retrieving the contextually relevant content from a repository server using one or more semantic search methodologies based on the dynamic psychological profile (204); e. generating one or more specialized prompts using the NPAI profiling, the content extraction, and the prompt devising units (205);f. processing the generated prompts through at least one Large Language Model (LLM) to generate a dynamically adapted personalized content (206); and g. displaying a personalized content to the user and collecting multimodal feedback for continuous adaptation (207).

8. The method (200) as claimed in claim 7, wherein the NPAI module implements a method of an active learning by analysing and dynamically prioritizing one or more user input data based on the needs of the individual user in real-time to generate the dynamic psychological profile of the user.

9. The method (200) as claimed in claim 7, wherein the analysis of the processed user data through the NPAI module comprises: i. integrating multiple data streams received from one or more neuroimaging devices, at least one visual data capture device, and a textual data capture device; ii. processing one or more psychological assessments; iii. analysing the user- specified preferences and interaction expectations.

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